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CovidSens: a vision on reliable social sensing for COVID-19
#MMPMID32836651
Rashid MT
; Wang D
Artif Intell Rev
2021[]; 54
(1
): 1-25
PMID32836651
show ga
With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has
becoming inherently important to disseminate accurate and timely information
about the disease. Due to the ubiquity of Internet connectivity and smart
devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to
extract real-time observations from online users. In this paper, we propose
CovidSens, a vision of social sensing-based risk alert systems to spontaneously
obtain and analyze social data to infer the state of the COVID-19 propagation.
CovidSens can actively help to keep the general public informed about the
COVID-19 spread and identify risk-prone areas by inferring future propagation
patterns. The CovidSens concept is motivated by three observations: (1) people
have been actively sharing their state of health and experience of the COVID-19
via online social media, (2) official warning channels and news agencies are
relatively slower than people reporting their observations and experiences about
COVID-19 on social media, and (3) online users are frequently equipped with
substantially capable mobile devices that are able to perform non-trivial
on-device computation for data processing and analytics. We envision an
unprecedented opportunity to leverage the posts generated by the ordinary people
to build a real-time sensing and analytic system for gathering and circulating
vital information of the COVID-19 propagation. Specifically, the vision of
CovidSens attempts to answer the questions: How to distill reliable information
about the COVID-19 with the coexistence of prevailing rumors and misinformation
in the social media? How to inform the general public about the latest state of
the spread timely and effectively, and alert them to remain prepared? How to
leverage the computational power on the edge devices (e.g., smartphones, IoT
devices, UAVs) to construct fully integrated edge-based social sensing platforms
for rapid detection of the COVID-19 spread? In this vision paper, we discuss the
roles of CovidSens and identify the potential challenges in developing reliable
social sensing-based risk alert systems. We envision that approaches originating
from multiple disciplines (e.g., AI, estimation theory, machine learning,
constrained optimization) can be effective in addressing the challenges. Finally,
we outline a few research directions for future work in CovidSens.
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